Guide to Contrastive Learning: Techniques, Models, and Applications
11 days ago
- #self-supervised-learning
- #machine-learning
- #contrastive-learning
- Contrastive learning is a self-supervised learning technique that maximizes similarity between positive data pairs and minimizes it for negative pairs.
- Self-supervised learning (SSL) can be divided into contrastive and non-contrastive methods, with examples like SimCLR (contrastive) and BYOL (non-contrastive).
- Key contrastive learning models include Contrastive Predictive Coding (CPC), SimCLR, Momentum Contrast (MoCo), and CLIP (Contrastive Language-Image Pretraining).
- SimCLR uses data augmentation and a non-linear projection (MLP) to improve performance, while MoCo treats contrastive learning as a dictionary lookup with momentum-based updates.
- CLIP combines image and text embeddings, training encoders to maximize similarity between correct image-caption pairs.
- Contrastive learning is useful for applications like zero-shot recognition, recommendation systems, document retrieval, and anomaly detection.
- Vector databases leverage contrastive learning embeddings for similarity-based tasks using metrics like cosine similarity or Euclidean distance.